An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia

Current technological advances are paving the way for technologies based on deep learning to be utilized in the majority of life fields. The effectiveness of these technologies has led them to be utilized in the medical field to classify and detect different diseases. Recently, the pandemic of coronavirus disease (COVID-19) has imposed considerable press on the health infrastructures all over the world. The reliable and early diagnosis of COVID-19-infected patients is crucial to limit and prevent its outbreak. COVID-19 diagnosis is feasible by utilizing reverse transcript-polymerase chain reaction testing;however, diagnosis utilizing chest x-ray radiography is deemed safe, reliable, and precise in various cases. © 2022 IGI Global. All rights reserved.

Lightweight Method for the Rapid Diagnosis of Coronavirus Disease 2019 from Chest X-ray Images using Deep Learning Technique

A rapid screening method is required for screening coronavirus disease 2019 (COVID-19) patients. Therefore, we proposed a model based on DenseNet-201 to detect and differentiate COVID-19 patients from normal people and patients with other bacterial/viral cases of pneumonia using chest X-ray images. Our four-class model was found to have an accuracy of 91.01 ± 1.86 (mean ± standard deviation) and a sensitivity of 92.65 ± 1.28 using a five-fold cross-validation method. Moreover, it was a relatively lightweight and robust model with a simplified structure and fewer parameters, training, and testing epochs. As a supplementary diagnosis tool, physicians can detect COVID-19 faster using this model. © 2021 IEEE.

Intelligent OpenFlow Switch for SDN Networks Based on COVID-19’s High Network Traffic using Heuristic GA-Fuzzification Control Approach

The COVID-19 outbreak has impacted network operators and data centers in terms of congestion and high traffic that lead to outages and significant pressure on the network. The overhead traffic is generated from web, voice calls, and Internet activity. In this paper, we are investigating data center congestion control for Software Defined Networks (SDN) network data centers. A Software-Defined (SDN) data center is an emerging networking paradigm that simplifies the network architecture by decentralizing plane functionality into a single with centralized decision capabilities. Along with the SDN paradigm, there is a crucial part that is responsible for forwarding packet called OpenFlow switching engine. In a typical SDN environment, the rules are initiated by the SDN controller and pushed to the OpenFlow switches. The traditional OpenFlow switch has no forwarding decision and depends on the incoming policies from the controller’s southbound interface. Additionally, the flow of traffic is initiated from different sources that are assigned to a specific route. However, this significant flow of traffic due to COVID-19 can lead to congestion and degradation of network performance in terms of delay and interruption. To be precise, a single OpenFlow switch could receive a capacity of traffic that floods its forwarding table and lead to link flaps and outages. In order to optimize the OpenFlow switch with regards to how much traffic it can host and to adjust routing capabilities for dynamic changes in the network, we propose an optimized OpenFlow congestion control and fault prediction framework for inbound traffic to overcome the inefficient route planning in the network. The proposed developed optimization algorithm is based on Genetic Evolutionary Algorithm criteria and adds intelligence to the OpenFlow switch by the adoption of Fuzzy Logic prediction capabilities. The experimental evaluation shows that the proposed optimization method adds significant intelligence and optimization to OpenFlow operation. The testbed was implemented experimentally using Raspberry Pi (RPI)cluster with customized SDN and OpenFlow deployment. The probability of the best fitness was 14.11% for Gen 999. The proposed approach adds intelligence and prediction into the OpenFlow switch to overcome the unstable flows of traffic and to predict faults to enhance the traffic capacity levels and manage flows into an entirely uninterrupted production environment. © 2022 IEEE.

Interventional radiology workflow and hospital occupancy in the pandemic era: areas for improvement

Purpose: Throughout the COVID-19 pandemic, an increasing hospital occupancy rate has been an ongoing issue, with several hospitals operating at or near full capacity. Emphasis has been placed to improve discharge strategies to maintain bed space and decrease hospital occupancy rate. The interventional radiology (IR) department can play a pivotal role in the discharge process by providing timely interventions that are essential prior to a patient’s discharge. This project aims to define the time intervals between the date of priority request for an IR procedure (in preparation to discharge), date of IR procedure, and date of patient’s actual discharge. Materials and Methods: Between April–September of 2021, a retrospective review of hospitalized patients in a tertiary medical center for whom an IR procedure labeled as “Priority Discharge” was requested by primary teams was performed. Multiple procedure-related variables, including time intervals between the placement of the order, and the patient’s actual discharge were recorded. Results: During the study period, a total of 75 IR procedure requests (42 male, 33 female, mean age of 60y, range 21-98y) were labeled as “Priority Discharge.” Overall 74 of 75 (99%) procedures were completed on the same day of request. Performed procedures were: peripherally inserted catheter (51%) midline (24%), tunneled hemodialysis catheter (16%), and other (9%). The average time interval that patients stayed in the hospital after the IR procedure was 3 days (SD: 4, Range of 0-20 days). Of the total 75 patients, 23 (31%) patients were discharged on the same day as the procedure, 33 (44%) patients were discharged within 1-4 days after the procedure, 12 (16%) patients were discharged within 5-9 days after the procedure, and 7 (9%) patients were discharged 10 or more days after the procedure. The average admission duration for the study population was 10 d (range 2-33 d). Conclusion: Due to the inherent complexity of the hospital operations, strategies aiming to prioritize IR procedures for patients pending discharge could help to improve hospitals’ occupancy rates. Nevertheless as shown in our study a considerable percent of these patients stay in hospital for several days after the procedure is complete. Inefficient application of this system could disrupt the triage of the requested procedures, which may eventually lead to an unnecessary delay for other patients and prolong their hospitalization. Accordingly, tools should be incorporated into these strategies that could improve IR workflow and decrease susceptibility of these strategies to miscommunication and errors.

Developments and Changes in Organization and Management Research in the Post-COVID-19 Era: A Foundationalist Approach

Today, management science, like all other areas of human life, has been severely affected by the coronavirus disease 2019 (COVID-19) crisis. The challenges that the crisis have created for various areas of management have had many consequences and in many cases have led to the loss and bankruptcy of many businesses. Hence, management theorists and practitioners need to address each of these challenges and issues in detail. Therefore, the year of the COVID-19 crisis has created a critical and epoch-making year for management knowledge. Accordingly, a big picture of changes caused by the COVID-19 pandemic in the organization and management studies is important. Thus, the chapter has pursued this important goal in three parts. In the first part, we examine theoretical foundations of organization and management that have changed due to the COVID-19 pandemic. The second part deals with organizational areas affected by the pandemic. In the last section, we will review the recommendations made by management theorists in the face of the pandemic. Finally, in the conclusion section, using the results of the studies conducted in these three sections, we focus on research currents in the organization and management studies faced with the pandemic. In this section, two research currents were identified: the revolutionary current and the developmental current. Each of these currents can totally transform the future of management theory and practice. Then, the effects of the COVID-19 pandemic on collapse and creation of businesses were examined. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Covidense: Providing a suitable solution for diagnosing Covid-19 lung infection based on deep learning from chest X-ray images of patients

Purpose: Coronavirus disease 2019 (Covid-19), first reported in December 2019 in Wuhan, China, has become a pandemic. Chest imaging is used for the diagnosis of Covid-19 patients and can address problems concerning Reverse Transcription-Polymerase Chain Reaction (RT-PCR) shortcomings. Chest X-ray images can act as an appropriate alternative to Computed Tomography (CT) for diagnosing Covid-19. The purpose of this study is to use a Deep Learning method for diagnosing Covid-19 cases using chest X-ray images. Thus, we propose Covidense based on the pre-trained Densenet-201 model and is trained on a dataset comprising chest X-ray images of Covid-19, normal, bacterial pneumonia, and viral pneumonia cases. Materials and Methods: In this study, a total number of 1280 chest X-ray images of Covid-19, normal, bacterial and viral pneumonia cases were collected from open access repositories. Covidense, a convolutional neural network model, is based on the pre-trained DenseNet-201 architecture, and after pre-processing the images, it has been trained and tested on the images using the 5-fold cross-validation method. Results: The accuracy of different classifications including classification of two classes (Covid-19, normal), three classes 1 (Covid-19, normal and bacterial pneumonia), three classes 2 (Covid-19, normal and viral pneumonia), and four classes (Covid-19, normal, bacterial pneumonia and viral pneumonia) are 99.46%, 92.86%, 93.91 %, and 91.01% respectively. Conclusion: This model can differentiate pneumonia caused by Covid-19 from other types of pneumonia, including bacterial and viral. The proposed model offers high accuracy and can be of great help for effective screening. Thus, reducing the rate of infection spread. Also, it can act as a complementary tool for the detection and diagnosis of Covid-19. Copyright © 2021 Tehran University of Medical Sciences.

AI and Robotics in the Fight Against COVID-19 Pandemic

The outbreak of the novel coronavirus and its disease COVID-19 present an unprecedented challenge for humanity. Artificial Intelligence (AI) and robotics may help fighting COVID-19. Potential applications of AI in this accelerating pandemic include, but are not limited to, early detection and diagnosis, massive agent modeling and simulation, data analytics, assistive robots, disinfection robots, public awareness and patrolling, contactless delivery services, virtual healthcare assistants, drug repurposing and vaccination discovery. This chapter sheds light on the roles AI and robotics can play in fighting this disastrous pandemic, and possible future ones, and highlights several potential applications to transform this challenge into opportunities. This chapter also discusses the ethical implications of AI and robotics during the pandemic and in the post-pandemic world. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Robotics and intelligent systems against a pandemic

The outbreak of the novel coronavirus and its disease COVID-19 presents an unprecedented challenge for humanity. Intelligent systems and robotics particularly are helping the fight against COVID-19 several ways. Potential technology-driven solutions in this accelerating pandemic include, but are not limited to, early detection and diagnosis, assistive robots, indoor and outdoor disinfection robots, public awareness and patrolling, contactless last-mile delivery services, micro-and nano-robotics and laboratory automation. This article sheds light on the roles robotics and automation can play in fighting this disastrous pandemic and highlights a number of potential applications to transform this challenge into opportunities. The article also highlights the ethical implications of robotics and intelligent systems during the emergency side and in the post-pandemic world. © 2021, Budapest Tech Polytechnical Institution. All rights reserved.

The application of ICT techs (mobile-assisted language learning, gamification, and virtual reality) in teaching english for secondary school students in malaysia during covid-19 pandemic

With the sudden changes caused by the ongoing worldwide pandemic in Malaysia and rapid progress in the educational learning system towards online learning, some teachers wondered about the application of technology in students' education and how it would impact their learning process. Therefore, the main objectives of this research are (1) to contextually understand the university interns' perception of ICT Techs (MALL, Gamification, and VR) in teaching English for secondary school students during the Covid-19 Pandemic in Malaysia, and (2) to determine which of these ICT Techs (MALL, Gamification, and VR) would be most preferred by the interns for teaching English to secondary school students in Malaysia. The research design for this study was quantitative and a web-based questionnaire was adapted from three articles (Mihaela Badea, 2015;Huseyin Oz, 2015;and Ali Rahimi, Niloofar Seyed Golshan & Hooman Mohebi, 2013). The reliability test indicated a value of 0.866 with Cronbach's Alpha reliability statistic. From a total of 63 university interns selected from a private university in Malaysia, the results indicated that 38.1% chose MALL, 33% chose Gamification, and 29% chose VR, as their preferred technology to teach English for secondary school students during this Covid-19 Pandemic in Malaysia. Evidence suggested that online learning can be more effective for students where they can control their own learning pace, compared to learning in a classroom environment. Thus, future English teachers should explore and apply innovative pedagogical methods in the teaching-learning process during pandemic outbreak in Malaysia, while contributing to the development of motivation, participation, and engagement among secondary school students in acquiring the English language. © 2020 by authors.